9 new features: substitution finder, sensory search, ingredient passport, mode wiki, cultural context, sibling alignment, recipe constellation, paper-stats heatmap, recipe coherence

app.py

@@ -528,6 +528,501 @@ def api_embed(ingredient, sibling="chem"):
     if ingredient not in m.vocab: return {"error": f"'{ingredient}' not in vocab"}
     return [float(x) for x in _unit(m.E[m.vocab[ingredient]]).tolist()]
 
+# =====================================================================
+# Inverse queries: substitution finder + sensory profile search
+# =====================================================================
+
+_SENSORY_SLIDER_KEYS = [
+    ("sweet",   ["sweet_score/", "cf_sweet/"]),
+    ("sour",    ["sour_score/",  "cf_sour/"]),
+    ("bitter",  ["bitter_score/","cf_bitter/"]),
+    ("umami",   ["umami_score/", "cf_umami/", "cf_meaty/"]),
+    ("fatty",   ["fatty_score/", "cf_fatty/"]),
+    ("pungent", ["pungent_score/", "cf_pungent/"]),
+    ("savory",  ["cf_savory/"]),
+    ("citrus",  ["cf_citrus/"]),
+    ("woody",   ["cf_woody/"]),
+    ("earthy",  ["cf_earthy/"]),
+]
+
+def _poles_with_prefix(m, prefix):
+    return [k for k in m.supervised_poles.keys() if k.startswith(prefix)]
+
+def _aggregate_pole(m, prefixes):
+    keys = []
+    for p in prefixes: keys.extend(_poles_with_prefix(m, p))
+    if not keys: return None
+    vecs = np.stack([_unit(m.supervised_poles[k]) for k in keys], axis=0)
+    return _unit(vecs.mean(axis=0))
+
+def _dominant_cuisine_pole(m, seed_name):
+    if seed_name not in m.vocab: return None
+    v = _unit(m.E[m.vocab[seed_name]])
+    best, best_sim = None, -1e9
+    for c in _CUISINES:
+        key = f"cuisine:{c}"
+        if key not in m.supervised_poles: continue
+        p = _unit(m.supervised_poles[key])
+        s = float(v @ p)
+        if s > best_sim: best_sim, best = s, p
+    return best
+
+def substitute_finder(seed, sibling, k, must_share_group, same_nova, diff_cuisine):
+    if not seed or seed not in MODELS[sibling].vocab:
+        return [], "_(pick a seed ingredient)_"
+    m = MODELS[sibling]
+    v = _unit(m.E[m.vocab[seed]])
+    q = v
+    notes_dir = []
+    if same_nova:
+        nova_keys = _poles_with_prefix(m, "nova_level/")
+        if nova_keys:
+            sims = [(k_, float(v @ _unit(m.supervised_poles[k_]))) for k_ in nova_keys]
+            top_key, _ = max(sims, key=lambda x: x[1])
+            q = _slerp(q, _unit(m.supervised_poles[top_key]), 15)
+            notes_dir.append("pulled toward NOVA peer")
+    if diff_cuisine:
+        d_cui = _dominant_cuisine_pole(m, seed)
+        if d_cui is not None:
+            q = _slerp(q, -d_cui, 30)
+            notes_dir.append("rotated 30° from dominant cuisine")
+    q = _unit(q)
+    seed_group = _NAME_TO_GROUP.get(seed, "Other")
+    wide = _topk(m, q, max(int(k) * 8, 40), exclude=[seed])
+    rows = []
+    for name, sim in wide:
+        grp = _NAME_TO_GROUP.get(name, "Other")
+        if must_share_group and grp != seed_group: continue
+        bits = [f"group: {grp}"] + notes_dir
+        rows.append([name, f"{sim:.4f}", grp, "; ".join(bits)])
+        if len(rows) >= int(k): break
+    return rows, f"Seed **{seed}** (group: {seed_group}). {len(rows)} substitutes."
+
+def sensory_search(sibling, k, *slider_values):
+    m = MODELS[sibling]
+    weights = dict(zip([lbl for lbl, _ in _SENSORY_SLIDER_KEYS], slider_values))
+    parts, used = [], []
+    for label, prefixes in _SENSORY_SLIDER_KEYS:
+        w = float(weights.get(label, 0.0))
+        if w <= 0: continue
+        pole = _aggregate_pole(m, prefixes)
+        if pole is None: continue
+        parts.append(w * pole)
+        used.append(f"{label}×{w:.2f}")
+    if not parts: return [], "_(move at least one slider above 0)_"
+    q = _unit(np.sum(parts, axis=0))
+    rows = [[n, f"{s:.4f}", _NAME_TO_GROUP.get(n, "Other")]
+            for n, s in _topk(m, q, int(k), exclude=[])]
+    return rows, "**Target axes:** " + " · ".join(used)
+
+# =====================================================================
+# Inspect: ingredient passport + mode wiki + cultural context
+# =====================================================================
+
+import matplotlib.gridspec as gridspec
+
+PASSPORT_SIBS = ["cooc", "core", "chem"]
+PASSPORT_SENS = ["sweet","sour","bitter","umami","fatty","pungent"]
+
+def _find_sensory_pole(m, axis):
+    for cand in (f"cf_{axis}", f"{axis}_score", axis):
+        if cand in m.supervised_poles:
+            return _unit(m.supervised_poles[cand])
+    # fuzzy
+    for k, v in m.supervised_poles.items():
+        if axis in k.lower():
+            return _unit(v)
+    return None
+
+def _sensory_profile(name):
+    out = {}
+    for axis in PASSPORT_SENS:
+        vals = []
+        for sib in PASSPORT_SIBS:
+            m = MODELS[sib]
+            if name not in m.vocab: continue
+            v = _unit(m.E[m.vocab[name]])
+            p = _find_sensory_pole(m, axis)
+            if p is not None: vals.append(float(v @ p))
+        out[axis] = float(np.mean(vals)) if vals else 0.0
+    return out
+
+def render_passport(name):
+    fig = plt.figure(figsize=(11.7, 16.5), facecolor=KAIKAKU_DARK)
+    fig.patch.set_facecolor(KAIKAKU_DARK)
+    gs = gridspec.GridSpec(6, 3, figure=fig,
+        height_ratios=[0.9, 0.4, 2.4, 2.0, 2.0, 0.4],
+        hspace=0.55, wspace=0.35, left=0.06, right=0.94, top=0.96, bottom=0.04)
+    def styled(ax, title=None):
+        ax.set_facecolor(KAIKAKU_DARK)
+        for s in ax.spines.values():
+            s.set_color(KAIKAKU_ACCENT_LIGHT); s.set_alpha(0.25)
+        ax.tick_params(colors=KAIKAKU_ACCENT_LIGHT, labelsize=8)
+        if title:
+            ax.set_title(title, color=KAIKAKU_ACCENT_LIGHT, fontsize=10,
+                         family="monospace", loc="left", pad=8)
+        return ax
+    # Headline
+    ax_h = fig.add_subplot(gs[0, :]); ax_h.axis("off")
+    pretty = (name or "").replace("_", " ").upper()
+    ax_h.text(0.0, 0.55, pretty, color=KAIKAKU_ACCENT_LIGHT,
+              fontsize=42, fontweight="bold", va="center")
+    group = _NAME_TO_GROUP.get(name, "Other")
+    ax_h.text(0.0, 0.05, f"INGREDIENT PASSPORT  ·  FOOD GROUP: {group.upper()}",
+              color=KAIKAKU_ACCENT, fontsize=11, family="monospace", va="center")
+    ax_h.plot([0, 1], [0.0, 0.0], color=KAIKAKU_ACCENT, lw=2, transform=ax_h.transAxes)
+    # Section label
+    ax_s = fig.add_subplot(gs[1, :]); ax_s.axis("off")
+    ax_s.text(0.0, 0.5, "// NEAREST NEIGHBOURS PER SIBLING",
+              color=KAIKAKU_ACCENT_LIGHT, fontsize=11, family="monospace", va="center")
+    # Neighbours
+    for i, sib in enumerate(PASSPORT_SIBS):
+        ax = styled(fig.add_subplot(gs[2, i]), title=f"[{sib.upper()}]")
+        ax.set_xticks([]); ax.set_yticks([])
+        m = MODELS[sib]
+        if name not in m.vocab:
+            ax.text(0.5, 0.5, "(not in vocab)", ha="center", va="center",
+                    color=KAIKAKU_ACCENT_LIGHT, transform=ax.transAxes, fontsize=10)
+            continue
+        q = _unit(m.E[m.vocab[name]])
+        for row, (nb, sim) in enumerate(_topk(m, q, 5, exclude=[name])):
+            y = 0.88 - row * 0.18
+            ax.text(0.04, y, nb.replace("_", " "), color="#FFFFFF",
+                    fontsize=11, family="monospace", transform=ax.transAxes)
+            ax.text(0.96, y, f"{sim:.3f}", color=KAIKAKU_ACCENT_LIGHT,
+                    fontsize=10, family="monospace", ha="right", transform=ax.transAxes)
+    # Sensory radar (polar inset)
+    ax_radar_host = fig.add_subplot(gs[3, 0]); ax_radar_host.axis("off")
+    ax_radar_host.text(0.0, 1.02, "// SENSORY RADAR",
+                       color=KAIKAKU_ACCENT_LIGHT, fontsize=11,
+                       family="monospace", transform=ax_radar_host.transAxes)
+    bb = ax_radar_host.get_position()
+    pad_x, pad_y = bb.width * 0.08, bb.height * 0.10
+    ax_polar = fig.add_axes(
+        [bb.x0 + pad_x, bb.y0 + pad_y, bb.width - 2*pad_x, bb.height - 2*pad_y - 0.01],
+        projection="polar")
+    sens = _sensory_profile(name)
+    theta = np.linspace(0, 2*np.pi, len(PASSPORT_SENS), endpoint=False)
+    r = np.array([max(0.0, sens[a]) for a in PASSPORT_SENS])
+    theta_c = np.concatenate([theta, theta[:1]])
+    r_c = np.concatenate([r, r[:1]])
+    ax_polar.set_facecolor(KAIKAKU_DARK)
+    ax_polar.plot(theta_c, r_c, color=KAIKAKU_ACCENT_LIGHT, lw=2)
+    ax_polar.fill(theta_c, r_c, color=KAIKAKU_ACCENT, alpha=0.35)
+    ax_polar.set_xticks(theta)
+    ax_polar.set_xticklabels([a.upper() for a in PASSPORT_SENS],
+                             color=KAIKAKU_ACCENT_LIGHT, fontsize=9, family="monospace")
+    ax_polar.set_yticklabels([])
+    ax_polar.set_ylim(0, max(0.6, float(r.max()) + 0.05))
+    ax_polar.grid(color=KAIKAKU_ACCENT_LIGHT, alpha=0.20, lw=0.6)
+    # Cuisine bar
+    ax_c = styled(fig.add_subplot(gs[3, 1:]), title="// CUISINE AFFILIATION  (chem)")
+    m = MODELS["chem"]
+    if name in m.vocab:
+        v = _unit(m.E[m.vocab[name]])
+        vals, labels = [], []
+        for cu in _CUISINES:
+            key = f"cuisine:{cu}"
+            if key in m.supervised_poles:
+                vals.append(float(v @ _unit(m.supervised_poles[key])))
+                labels.append(cu.replace("_", " "))
+        y_pos = np.arange(len(labels))
+        bar_colors = [KAIKAKU_ACCENT_LIGHT if x >= 0 else "#F4B86E" for x in vals]
+        ax_c.barh(y_pos, vals, color=bar_colors, edgecolor=KAIKAKU_ACCENT, linewidth=0.6)
+        ax_c.set_yticks(y_pos); ax_c.set_yticklabels(labels, color="#FFFFFF", fontsize=9)
+        ax_c.axvline(0, color=KAIKAKU_ACCENT_LIGHT, alpha=0.4, lw=0.8)
+        ax_c.invert_yaxis()
+    else:
+        ax_c.text(0.5, 0.5, "(not in chem vocab)", ha="center", va="center",
+                  color=KAIKAKU_ACCENT_LIGHT, transform=ax_c.transAxes)
+    # Closest 3 emergent modes
+    ax_m = styled(fig.add_subplot(gs[4, :]),
+                 title="// CLOSEST EMERGENT FACTOR MODES  (top 3 across siblings)")
+    ax_m.set_xticks([]); ax_m.set_yticks([])
+    scored = []
+    for sib in PASSPORT_SIBS:
+        m = MODELS[sib]
+        if name not in m.vocab: continue
+        v = _unit(m.E[m.vocab[name]])
+        for md in m.modes:
+            if md.kind != "factor": continue
+            scored.append((float(_unit(md.pole) @ v), sib, md))
+    scored.sort(key=lambda x: -x[0])
+    for row, (sim, sib, md) in enumerate(scored[:3]):
+        y = 0.82 - row * 0.30
+        ax_m.text(0.01, y, f"[{sib.upper()}]", color=KAIKAKU_ACCENT,
+                  fontsize=11, family="monospace", transform=ax_m.transAxes)
+        ax_m.text(0.10, y, md.label, color="#FFFFFF",
+                  fontsize=13, fontweight="bold", transform=ax_m.transAxes)
+        ax_m.text(0.99, y, f"cos {sim:.3f}", color=KAIKAKU_ACCENT_LIGHT,
+                  fontsize=10, family="monospace", ha="right", transform=ax_m.transAxes)
+        members = ", ".join(md.members[:6])
+        ax_m.text(0.10, y - 0.10, members, color=KAIKAKU_ACCENT_LIGHT,
+                  fontsize=9, family="monospace", transform=ax_m.transAxes)
+    # Footer
+    ax_f = fig.add_subplot(gs[5, :]); ax_f.axis("off")
+    ax_f.text(0.5, 0.5, "EPICURE  ·  300-D INGREDIENT EMBEDDING  ·  KAIKAKU",
+              ha="center", va="center", color=KAIKAKU_ACCENT,
+              fontsize=9, family="monospace", transform=ax_f.transAxes)
+    return fig
+
+
+def _mode_choices_searchable(sibling):
+    m = MODELS[sibling]
+    out = []
+    for md in m.modes:
+        z = f"  z={md.prop_z_mean:+.2f}" if isinstance(md.prop_z_mean, (int, float)) else ""
+        out.append((f"[{md.kind}] {md.label} ({md.mode_id}, n={md.n_members}{z})", md.mode_id))
+    return sorted(out, key=lambda x: x[0].lower())
+
+def render_mode_wiki(sibling, mode_id):
+    if not sibling or not mode_id: return "_Pick a mode._"
+    m = MODELS[sibling]
+    target = next((md for md in m.modes if md.mode_id == mode_id), None)
+    if target is None: return f"_Mode `{mode_id}` not found._"
+    pole = _unit(target.pole)
+    members = [n for n in (target.members or []) if n in m.vocab]
+    if members:
+        idxs = np.array([m.vocab[n] for n in members])
+        sims_mem = m.E[idxs] @ pole
+        members = [members[i] for i in np.argsort(-sims_mem)]
+    related = []
+    for md in m.modes:
+        if md.mode_id == target.mode_id: continue
+        related.append((md, float(_unit(md.pole) @ pole)))
+    related.sort(key=lambda x: -x[1])
+    sup = sorted(((k, float(_unit(v) @ pole)) for k, v in m.supervised_poles.items()),
+                 key=lambda x: -x[1])[:3]
+    spotlight = ", ".join(f"**{n.replace('_',' ')}**" for n in members[:3]) or "_(none)_"
+    out = [f"## {target.label}",
+           f"`{target.mode_id}` · sibling **{sibling}** · kind **{target.kind}** · "
+           f"property **{target.property}** · members **{target.n_members}**",
+           f"\n### Spotlight\n{spotlight}",
+           "\n### All members (cosine-ordered)",
+           ", ".join(n.replace("_", " ") for n in members) or "_(none)_",
+           "\n### Closest related modes",
+           "| mode_id | label | kind | cosine |", "|---|---|---|---:|"]
+    for md, sim in related[:5]:
+        out.append(f"| `{md.mode_id}` | {md.label} | {md.kind} | {sim:.3f} |")
+    out.append("\n### Top supervised directions")
+    out.append("| direction | cosine |"); out.append("|---|---:|")
+    for k, sim in sup: out.append(f"| `{k}` | {sim:.3f} |")
+    return "\n".join(out)
+
+
+def _load_cuisine_taxonomy():
+    """Try to load the cuisine_macroregions.json shipped with the corpus dataset; fall back to inline."""
+    try:
+        from huggingface_hub import hf_hub_download
+        p = hf_hub_download("Kaikaku/epicure-corpus-resources",
+                            "data/cuisine_macroregions.json", repo_type="dataset")
+        return json.loads(open(p).read())
+    except Exception:
+        return {
+            "East_Asian": {"traditions": ["Chinese", "Korean"]},
+            "Western_Atlantic": {"traditions": ["American","British","German","Scandinavian"]},
+            "Mediterranean": {"traditions": ["Italian","French","Iberian","Greek","Levantine","North African","Turkish"]},
+            "Eastern_European": {"traditions": ["Russian","Ukrainian","Polish","Hungarian","Georgian"]},
+            "Southeast_Asian": {"traditions": ["Thai","Vietnamese","Filipino","Indonesian","Malay"]},
+            "South_Asian": {"traditions": ["Indian","Pakistani","Sri Lankan","Bangladeshi"]},
+            "Latin_American": {"traditions": ["Mexican","Caribbean","Brazilian","Peruvian","Colombian"]},
+            "Japanese": {"traditions": ["Japanese"]},
+        }
+
+_CUISINE_TAXONOMY = _load_cuisine_taxonomy()
+
+def cultural_context(ingredient, sibling="chem", k=4):
+    if not ingredient or ingredient not in MODELS[sibling].vocab:
+        return [], "_(pick an ingredient)_"
+    m = MODELS[sibling]
+    v = _unit(m.E[m.vocab[ingredient]])
+    scored = []
+    for c in _CUISINES:
+        key = f"cuisine:{c}"
+        if key in m.supervised_poles:
+            scored.append((c, float(v @ _unit(m.supervised_poles[key]))))
+    scored.sort(key=lambda x: -x[1])
+    top = scored[:int(k)]
+    rows = []
+    all_trads = []
+    for region, sim in top:
+        trads = _CUISINE_TAXONOMY.get(region, {}).get("traditions", [])
+        all_trads.extend(trads)
+        rows.append([region.replace("_", " "), f"{sim:+.3f}", ", ".join(trads)])
+    md = (f"**{ingredient}** aligns most strongly with these culinary traditions:  \n"
+          f"{', '.join(sorted(set(all_trads)))}.\n\n"
+          f"_The Epicure paper normalised source-language recipes to a single English canonical vocabulary; "
+          f"per-language ingredient names were not persisted. This view surfaces the **culinary geography** "
+          f"the model learned instead._")
+    return rows, md
+
+
+# =====================================================================
+# Constellations: sibling alignment + recipe constellation
+# =====================================================================
+
+def render_sibling_alignment(ingredient):
+    if not ingredient or ingredient not in MODELS["cooc"].vocab:
+        fig, ax = _gallery_axes(figsize=(16, 5))
+        ax.text(0.5, 0.5, "Pick an ingredient", ha="center", va="center",
+                transform=ax.transAxes, color=GALLERY_TXTDIM)
+        return fig
+    sibs = ["cooc", "core", "chem"]
+    fig, axes = plt.subplots(1, 3, figsize=(16, 5), facecolor=GALLERY_BG,
+                             gridspec_kw=dict(wspace=0.06))
+    top1 = []
+    try: from scipy.stats import gaussian_kde
+    except Exception: gaussian_kde = None
+    for ax, sib in zip(axes, sibs):
+        ax.set_facecolor(GALLERY_BG)
+        for s in ax.spines.values(): s.set_visible(False)
+        ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False)
+        m = MODELS[sib]; coords = UMAP_DATA[sib]
+        xmin, xmax = float(coords[:,0].min()-0.6), float(coords[:,0].max()+0.6)
+        ymin, ymax = float(coords[:,1].min()-0.6), float(coords[:,1].max()+0.6)
+        if gaussian_kde is not None:
+            try:
+                kde = gaussian_kde(coords.T, bw_method=0.20)
+                xx, yy = np.meshgrid(np.linspace(xmin, xmax, 120), np.linspace(ymin, ymax, 120))
+                zz = kde(np.vstack([xx.ravel(), yy.ravel()])).reshape(xx.shape)
+                ax.contour(xx, yy, zz, levels=12, colors=GALLERY_GRID, alpha=0.45, linewidths=0.55)
+            except Exception: pass
+        ax.scatter(coords[:,0], coords[:,1], s=2, c=GALLERY_DUST, alpha=0.5, linewidths=0, zorder=2)
+        q = _unit(m.E[m.vocab[ingredient]])
+        nb = _topk(m, q, 5, exclude=[ingredient])
+        top1.append(nb[0][0] if nb else "-")
+        for nm, _s in nb:
+            p = coords[m.vocab[nm]]
+            ax.scatter([p[0]], [p[1]], s=110, c="#F4B86E", edgecolors="white", linewidths=0.7, zorder=4)
+            ax.text(p[0]+0.10, p[1]+0.10, nm, color=GALLERY_TEXT, fontsize=8.5, alpha=0.95, zorder=5)
+        sp = coords[m.vocab[ingredient]]
+        ax.scatter([sp[0]], [sp[1]], s=380, c=KAIKAKU_ACCENT_LIGHT, marker="*",
+                   edgecolors="white", linewidths=1.0, zorder=6)
+        ax.text(sp[0], sp[1]+0.32, ingredient, color=KAIKAKU_ACCENT_LIGHT, ha="center",
+                fontsize=10.5, fontweight="bold", zorder=7)
+        ax.set_xlim(xmin, xmax); ax.set_ylim(ymin, ymax); ax.set_aspect("equal")
+        ax.set_title(f"{sib.upper()} · {ingredient}", color=GALLERY_TXTDIM,
+                     fontsize=11, family="monospace", pad=6)
+    sub = "   ".join(f"{s.upper()}→{t}" for s, t in zip(sibs, top1))
+    fig.suptitle(f"SIBLING ALIGNMENT  ·  top-1 per sibling:   {sub}",
+                 color=GALLERY_TXTDIM, fontsize=11, family="monospace", y=0.02)
+    plt.tight_layout(rect=[0, 0.04, 1, 0.97])
+    return fig
+
+
+def render_recipe_constellation(sibling, ingredients):
+    m = MODELS[sibling]; coords = UMAP_DATA[sibling]
+    fig, ax = _gallery_axes(figsize=(11, 9))
+    xmin, xmax = float(coords[:,0].min()-0.6), float(coords[:,0].max()+0.6)
+    ymin, ymax = float(coords[:,1].min()-0.6), float(coords[:,1].max()+0.6)
+    try:
+        from scipy.stats import gaussian_kde
+        kde = gaussian_kde(coords.T, bw_method=0.20)
+        xx, yy = np.meshgrid(np.linspace(xmin, xmax, 140), np.linspace(ymin, ymax, 140))
+        zz = kde(np.vstack([xx.ravel(), yy.ravel()])).reshape(xx.shape)
+        ax.contour(xx, yy, zz, levels=10, colors=GALLERY_GRID, alpha=0.4, linewidths=0.5)
+    except Exception: pass
+    ax.scatter(coords[:,0], coords[:,1], s=2, c=GALLERY_DUST, alpha=0.5, linewidths=0, zorder=2)
+    valid = [n for n in (ingredients or []) if n in m.vocab]
+    if not valid:
+        ax.text(0.5, 0.5, "Pick ingredients", ha="center", va="center",
+                transform=ax.transAxes, color=GALLERY_TXTDIM)
+        ax.set_xlim(xmin, xmax); ax.set_ylim(ymin, ymax); ax.set_aspect("equal"); return fig
+    idxs = [m.vocab[n] for n in valid]
+    pts = coords[idxs]
+    vecs = np.stack([_unit(m.E[i]) for i in idxs])
+    n = len(valid)
+    degree = np.zeros(n, dtype=int)
+    if n >= 2:
+        sim = vecs @ vecs.T
+        np.fill_diagonal(sim, -np.inf)
+        k_near = min(2, n - 1)
+        edges = set()
+        for i in range(n):
+            for j in np.argsort(-sim[i])[:k_near]:
+                a, b = sorted((i, int(j)))
+                if a == b or (a, b) in edges: continue
+                edges.add((a, b)); degree[a] += 1; degree[b] += 1
+        from matplotlib.patches import FancyArrowPatch
+        for (a, b) in edges:
+            w = max(0.0, float(sim[a, b]))
+            alpha = float(np.clip(0.25 + 0.65 * w, 0.15, 0.9))
+            arc = FancyArrowPatch((pts[a,0], pts[a,1]), (pts[b,0], pts[b,1]),
+                connectionstyle="arc3,rad=0.18", arrowstyle="-",
+                color=KAIKAKU_ACCENT_LIGHT, lw=0.9, alpha=alpha, zorder=3)
+            ax.add_patch(arc)
+    sizes = 140 + 90 * degree
+    ax.scatter(pts[:,0], pts[:,1], s=sizes, c="#F4B86E", marker="*",
+               edgecolors="white", linewidths=0.9, zorder=5)
+    for name, p in zip(valid, pts):
+        ax.text(p[0], p[1]+0.30, name, color=GALLERY_TEXT, ha="center",
+                fontsize=9.5, fontweight="bold", zorder=6)
+    ax.set_xlim(xmin, xmax); ax.set_ylim(ymin, ymax); ax.set_aspect("equal")
+    ax.text(0.02, 0.97, f"RECIPE CONSTELLATION · {sibling.upper()}",
+            transform=ax.transAxes, color=GALLERY_TXTDIM,
+            fontsize=11, family="monospace", va="top")
+    fig.text(0.5, 0.04, "  ·  ".join(valid), ha="center", color=GALLERY_TEXT, fontsize=10)
+    plt.tight_layout(rect=[0, 0.06, 1, 1])
+    return fig
+
+
+# =====================================================================
+# Simpler additions: recipe coherence rating + direction-quality heatmap
+# =====================================================================
+
+def recipe_coherence(sibling, basket):
+    """Return mean pairwise cosine within basket + a rating label + a tiny bar visual."""
+    m = MODELS[sibling]
+    valid = [n for n in (basket or []) if n in m.vocab]
+    if len(valid) < 2:
+        return "_Add 2+ ingredients to score._"
+    idxs = [m.vocab[n] for n in valid]
+    sub = m.E[idxs] / np.linalg.norm(m.E[idxs], axis=1, keepdims=True)
+    sim = sub @ sub.T
+    np.fill_diagonal(sim, np.nan)
+    mean_sim = float(np.nanmean(sim))
+    # rating bands
+    if mean_sim < 0.10: rating = "Scattered (very diverse)"
+    elif mean_sim < 0.25: rating = "Eclectic"
+    elif mean_sim < 0.40: rating = "Coherent"
+    elif mean_sim < 0.55: rating = "Tightly coherent"
+    else: rating = "Possibly redundant"
+    pct = int(np.clip(mean_sim, 0, 1) * 100)
+    bar = "█" * (pct // 5) + "░" * (20 - pct // 5)
+    return (f"**Recipe coherence:** mean pairwise cosine = `{mean_sim:.3f}`  \n"
+            f"**Rating:** {rating}  \n"
+            f"`{bar}` {pct}%")
+
+
+def render_direction_quality_heatmap():
+    """Paper §3.2 table as a Plotly heatmap."""
+    probes = [
+        ("CF baked-in", "Compound feature (seen by Core/Chem walk schema)", [0.28, 0.40, 0.46]),
+        ("CF basic-taste (held-out)", "Sweet/sour/bitter/salt/umami", [0.32, 0.42, 0.47]),
+        ("USDA macros", "Protein/fat/carb/fibre/etc.", [0.41, 0.45, 0.49]),
+        ("Cuisine (Cohen's d)", "8 macro-regions, one-vs-rest", [2.43, 2.70, 3.07]),
+    ]
+    z = np.array([row[2] for row in probes])
+    text = [[f"{v:.2f}" for v in row] for row in z]
+    fig = go.Figure(go.Heatmap(
+        z=z, x=["Cooc", "Core", "Chem"], y=[r[0] for r in probes],
+        text=text, texttemplate="%{text}", textfont=dict(size=14, color="white"),
+        colorscale=[[0, GALLERY_BG], [0.3, GALLERY_DUST], [0.7, KAIKAKU_ACCENT], [1, KAIKAKU_ACCENT_LIGHT]],
+        showscale=True, colorbar=dict(title="score"),
+        hovertemplate="<b>%{y}</b><br>%{x}: %{z:.3f}<extra></extra>",
+    ))
+    fig.update_layout(
+        title=dict(text="DIRECTION QUALITY  ·  Cooc &lt; Core &lt; Chem (paper §3.2)",
+                   font=dict(size=14, color=GALLERY_TEXT, family="monospace")),
+        paper_bgcolor=GALLERY_BG, plot_bgcolor=GALLERY_BG,
+        height=380, margin=dict(l=180, r=40, t=70, b=40),
+        font=dict(color=GALLERY_TEXT),
+        xaxis=dict(tickfont=dict(color=GALLERY_TEXT, size=12)),
+        yaxis=dict(tickfont=dict(color=GALLERY_TEXT, size=11), autorange="reversed"),
+    )
+    return fig
+
+
 # =====================================================================
 # Gallery: six aesthetic visualisations.
 # All use a shared dark-teal Kaikaku palette so they hang together.
@@ -1247,6 +1742,134 @@ Three sibling ingredient embeddings from [arXiv:2605.22391](https://arxiv.org/ab
                 label="Try one of these",
             )
 
+        # ---------- Tab: INVERSE QUERIES ----------
+        with gr.Tab("Inverse queries"):
+            with gr.Tabs():
+                with gr.Tab("Substitution finder"):
+                    gr.Markdown("I'm out of X — what's the closest substitute? Optional constraints.")
+                    with gr.Row():
+                        sub_seed = gr.Dropdown(choices=ALL_INGREDIENTS, label="Seed ingredient", value="mascarpone_cheese")
+                        sub_sib = gr.Dropdown(choices=["cooc","core","chem"], value="chem", label="Sibling")
+                        sub_k = gr.Slider(3, 20, value=10, step=1, label="K")
+                    with gr.Row():
+                        sub_grp = gr.Checkbox(label="Must share food group", value=True)
+                        sub_nova = gr.Checkbox(label="Pull toward same NOVA level", value=False)
+                        sub_cui = gr.Checkbox(label="Rotate 30° from dominant cuisine", value=False)
+                    sub_btn = gr.Button("Find substitutes", variant="primary")
+                    sub_df = gr.Dataframe(headers=["Substitute","Cosine","Food group","Notes"],
+                                          wrap=True, label="Top-K substitutes")
+                    sub_md = gr.Markdown()
+                    sub_btn.click(substitute_finder,
+                                  inputs=[sub_seed, sub_sib, sub_k, sub_grp, sub_nova, sub_cui],
+                                  outputs=[sub_df, sub_md], show_progress="minimal")
+                    gr.Examples(
+                        examples=[
+                            ["mascarpone_cheese","chem",10,True,False,False],
+                            ["fish_sauce","chem",10,False,False,False],
+                            ["saffron","chem",8,False,False,True],
+                            ["beef","core",8,True,False,False],
+                        ],
+                        inputs=[sub_seed, sub_sib, sub_k, sub_grp, sub_nova, sub_cui],
+                        label="Try one",
+                    )
+
+                with gr.Tab("Sensory profile search"):
+                    gr.Markdown("Drag sliders for the sensory axes you want; tool returns ingredients matching that profile.")
+                    with gr.Row():
+                        sp_sib = gr.Dropdown(choices=["cooc","core","chem"], value="chem", label="Sibling")
+                        sp_k = gr.Slider(5, 30, value=15, step=1, label="K")
+                    sp_sliders = []
+                    with gr.Row():
+                        for label, _ in _SENSORY_SLIDER_KEYS[:5]:
+                            sp_sliders.append(gr.Slider(0, 1, value=0, step=0.05, label=label))
+                    with gr.Row():
+                        for label, _ in _SENSORY_SLIDER_KEYS[5:]:
+                            sp_sliders.append(gr.Slider(0, 1, value=0, step=0.05, label=label))
+                    sp_btn = gr.Button("Search by profile", variant="primary")
+                    sp_df = gr.Dataframe(headers=["Ingredient","Cosine","Food group"], wrap=True, label="Top-K")
+                    sp_md = gr.Markdown()
+                    sp_btn.click(sensory_search, inputs=[sp_sib, sp_k, *sp_sliders],
+                                 outputs=[sp_df, sp_md], show_progress="minimal")
+
+        # ---------- Tab: INSPECT ----------
+        with gr.Tab("Inspect"):
+            with gr.Tabs():
+                with gr.Tab("Ingredient passport"):
+                    gr.Markdown("Single-page dossier for one ingredient. Tweetable PNG.")
+                    with gr.Row():
+                        pp_pick = gr.Dropdown(choices=ALL_INGREDIENTS, label="Ingredient", value="basil")
+                        pp_btn = gr.Button("Generate passport", variant="primary")
+                    pp_plot = gr.Plot(label="")
+                    pp_btn.click(render_passport, inputs=[pp_pick], outputs=[pp_plot], show_progress="full")
+                    pp_pick.change(render_passport, inputs=[pp_pick], outputs=[pp_plot], show_progress="minimal")
+                    # render initial value
+                    demo.load(render_passport, inputs=[pp_pick], outputs=[pp_plot])
+
+                with gr.Tab("Mode wiki"):
+                    gr.Markdown("Click into any of the ~500 modes for a per-mode wiki page.")
+                    with gr.Row():
+                        wk_sib = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling")
+                        wk_mode = gr.Dropdown(choices=_mode_choices_searchable("chem"),
+                                              label="Mode", filterable=True)
+                    wk_md = gr.Markdown()
+                    wk_sib.change(lambda s: gr.Dropdown(choices=_mode_choices_searchable(s), value=None),
+                                  inputs=[wk_sib], outputs=[wk_mode])
+                    wk_mode.change(render_mode_wiki, inputs=[wk_sib, wk_mode], outputs=[wk_md])
+
+                with gr.Tab("Cultural context"):
+                    gr.Markdown("Map an ingredient to its cuisine traditions. Paper-grounded; English-only because the source-language names were not persisted by the LLM pipeline.")
+                    with gr.Row():
+                        cx_ing = gr.Dropdown(choices=ALL_INGREDIENTS, label="Ingredient", value="gochujang" if "gochujang" in MODELS["chem"].vocab else "miso")
+                        cx_sib = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling")
+                        cx_k = gr.Slider(2, 6, value=4, step=1, label="Top-K cuisines")
+                        cx_btn = gr.Button("Show context", variant="primary")
+                    cx_df = gr.Dataframe(headers=["Macro-region","Cosine","Constituent traditions"],
+                                         wrap=True, label="Closest cuisine macro-regions")
+                    cx_md = gr.Markdown()
+                    cx_btn.click(cultural_context, inputs=[cx_ing, cx_sib, cx_k],
+                                 outputs=[cx_df, cx_md], show_progress="minimal")
+                    cx_ing.change(cultural_context, inputs=[cx_ing, cx_sib, cx_k],
+                                  outputs=[cx_df, cx_md])
+
+        # ---------- Tab: CONSTELLATIONS ----------
+        with gr.Tab("Constellations"):
+            with gr.Tabs():
+                with gr.Tab("Sibling alignment"):
+                    gr.Markdown("Same ingredient on all three UMAPs. The spectrum-of-models thesis as a single image.")
+                    with gr.Row():
+                        sa_ing = gr.Dropdown(choices=ALL_INGREDIENTS, value="basil", label="Ingredient")
+                        sa_btn = gr.Button("Render", variant="primary")
+                    sa_plot = gr.Plot(label="")
+                    sa_btn.click(render_sibling_alignment, inputs=[sa_ing], outputs=[sa_plot], show_progress="full")
+                    sa_ing.change(render_sibling_alignment, inputs=[sa_ing], outputs=[sa_plot])
+                    demo.load(render_sibling_alignment, inputs=[sa_ing], outputs=[sa_plot])
+
+                with gr.Tab("Recipe constellation"):
+                    gr.Markdown("A recipe drawn as a constellation on the UMAP, edges to nearest basket-mates.")
+                    with gr.Row():
+                        rc_sib = gr.Radio(choices=["cooc","core","chem"], value="chem", label="Sibling")
+                        rc_ings = gr.Dropdown(choices=ALL_INGREDIENTS, multiselect=True,
+                                              value=["tomato","basil","garlic","olive_oil","mozzarella_cheese"],
+                                              label="Recipe ingredients", max_choices=12)
+                        rc_btn = gr.Button("Render", variant="primary")
+                    rc_plot = gr.Plot(label="")
+                    rc_btn.click(render_recipe_constellation, inputs=[rc_sib, rc_ings], outputs=[rc_plot], show_progress="full")
+                    demo.load(render_recipe_constellation, inputs=[rc_sib, rc_ings], outputs=[rc_plot])
+                    gr.Examples(
+                        examples=[
+                            ["chem", ["tomato","basil","garlic","olive_oil","mozzarella_cheese"]],
+                            ["chem", ["chicken","lemongrass","coconut_milk","fish_sauce","lime","ginger","chili_pepper"]],
+                            ["chem", ["beef","cumin","coriander","onion","garlic","tomato","cinnamon"]],
+                            ["core", ["chocolate","strawberry","cream","sugar","vanilla"]],
+                        ],
+                        inputs=[rc_sib, rc_ings], label="Try a recipe",
+                    )
+
+        # ---------- Tab: PAPER STATS ----------
+        with gr.Tab("Paper stats"):
+            gr.Markdown("Headline numbers from the paper §3.2. The Cooc < Core < Chem ordering on every probe stratum.")
+            dq_plot = gr.Plot(value=render_direction_quality_heatmap(), label="")
+
         # ---------- Tab 5: GALLERY ----------
         with gr.Tab("Gallery"):
             gr.Markdown("Six aesthetic views of the model. All rendered in the Kaikaku palette.")